RDF description Dr. Unai Zulaika Zurimendi

?  -  Present
unai.zulaika [at] deusto.es

[u' @article{zulaika_zurimendi_regularized_2022, title = {Regularized online tensor factorization for sparse knowledge graph embeddings}, issn = {1433-3058}, url = {https://doi.org/10.1007/s00521-022-07796-z}, doi = {10.1007/s00521-022-07796-z}, abstract = {Knowledge Graphs represent real-world facts and are used in several applications; however, they are often incomplete and have many missing facts. Link prediction is the task of completing these missing facts from existing ones. Embedding models based on Tensor Factorization attain state-of-the-art results in link prediction. Nevertheless, the embeddings they produce can not be easily interpreted. Inspired by previous work on word embeddings, we propose inducing sparsity in the bilinear tensor factorization model, RESCAL, to build interpretable Knowledge Graph embeddings. To overcome the difficulties that stochastic gradient descent has when producing sparse solutions, we add \\$\\$l\\_1\\$\\$regularization to the learning objective by using the generalized Regularized Dual Averaging online optimization algorithm. The proposed method substantially improves the interpretability of the learned embeddings while maintaining competitive performance in the standard metrics.}, language = {en}, urldate = {2022-09-30}, journal = {Neural Computing and Applications}, author = {Zulaika Zurimendi, Unai and Almeida, Aitor and L\xf3pez-de-Ipi\xf1a, Diego}, month = sep, year = {2022}, keywords = {Interpretable embeddings, Knowledge graph embedding, Sparse learning, inception, jcr5.102, kno, knowledge graphs, machine learning, q1, representation learning}, } ']

[u' @article{zulaika_zurimendi_lwp-wl_2022, title = {{LWP}-{WL}: {Link} weight prediction based on {CNNs} and the {Weisfeiler}-{Lehman} algorithm}, issn = {1568-4946}, shorttitle = {{LWP}-{WL}}, url = {https://www.sciencedirect.com/science/article/pii/S156849462200134X}, doi = {10.1016/j.asoc.2022.108657}, abstract = {We present a new technique for link weight prediction, the Link Weight Prediction Weisfeiler-Lehman (LWP-WL) method that learns from graph structure features and link relationship patterns. Inspired by the Weisfeiler-Lehman Neural Machine, LWP-WL extracts an enclosing subgraph for the target link and applies a graph labelling algorithm for weighted graphs to provide an ordered subgraph adjacency matrix into a neural network. The neural network contains a Convolutional Neural Network in the first layer that applies special filters adapted to the input graph representation. An extensive evaluation is provided that demonstrates an improvement over the state-of-the-art methods in several weighted graphs. Furthermore, we conduct an ablation study to show how adding different features to our approach improves our technique\u2019s performance. Finally, we also perform a study on the complexity and scalability of our algorithm. Unlike other approaches, LWP-WL does not rely on a specific graph heuristic and can perform well in different kinds of graphs.}, language = {en}, urldate = {2022-03-08}, journal = {Applied Soft Computing}, author = {Zulaika Zurimendi, Unai and S\xe1nchez-Corcuera, Rub\xe9n and Almeida, Aitor and L\xf3pez-de-Ipi\xf1a, Diego}, month = feb, year = {2022}, keywords = {FuturAAL, Graph mining, JCR6.725, Link weight prediction, Q1, SentientThings, Weisfeiler-Lehman algorithm, artificial intelligence, graph analysis, graph convolutional networks, link prediction, machine learning}, pages = {108657}, } ']

[u' @inproceedings{zulaika_zurimendi_influence_2022, title = {Influence {Functions} for {Interpretable} link prediction in {Knowledge} {Graphs} for {Intelligent} {Environments}}, isbn = {978-953-290-115-3}, abstract = {Knowledge graphs are large, graph-structured databases used in many use-case scenarios such as Intelligent Environments. Many Artificial Intelligent latent feature models are used to infer new facts in Knowledge Graphs. Despite their success, the lack of interpretability remains a challenge to overcome. This paper applies influence functions to obtain the most significant facts when predicting new knowledge and allows users to understand these models. However, Influence Functions do not scale well. We present an efficient method to scale up influence functions to large Knowledge Graphs to overcome such an issue. It drastically reduces the number of training samples when computing influences and uses fast curvature matrixvector products to linearize the computation steps required for the inverse Hessian. We conduct experiments on different sized Knowledge Graphs demonstrating the scalability of our approach and its effectiveness in measuring the most influential facts. Our method provides an intuitive understanding of link prediction behaviour in Knowledge Graphs and Intelligent Environments.}, language = {en}, booktitle = {Proceedings of the 7th {International} {Conference} on {Smart} and {Sustainable} {Technologies} ({Splitech} 2022)}, author = {Zulaika Zurimendi, Unai and Almeida, Aitor and Lopez-de-Ipina, Diego}, month = apr, year = {2022}, keywords = {Interpretability, inception, influence functions, knowledge graphs, link prediction, machine learning, representation learning}, pages = {7}, } ']

[u' @article{sanchez-corcuera_analysing_2021, title = {Analysing centralities for organisational role inference in online social networks}, volume = {99}, issn = {09521976}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0952197620303663}, doi = {10.1016/j.engappai.2020.104129}, abstract = {The intensive use of Online Social Networks (OSN) nowadays has made users expose more information without realising it. Malicious users or marketing agencies are now able to infer information that is not published on OSNs by using data from targets friends to use for their benefit. In this paper, the authors present a generalisable method capable of deducing the roles of employees of an organisation using their Twitter relationships and the features of the graph from their organisation. The authors also conduct an extensive analysis of the node centralities to study their roles in the inference of the different classes proposed. Derived from the experiments and the ablation study conducted to the centralities, the authors conclude that the latent features of the graph along with the directed relationships perform better than previously proposed methods when classifying the role of the employees of an organisation. Additionally, to evaluate the method, the authors also contribute with a new dataset consisting of three directed graphs (one for each organisation) representing the relationships between the employees obtained from Twitter.}, language = {en}, urldate = {2021-01-04}, journal = {Engineering Applications of Artificial Intelligence}, author = {S\xe1nchez-Corcuera, Rub\xe9n and Bilbao-Jayo, Aritz and Zulaika, Unai and Almeida, Aitor}, month = mar, year = {2021}, keywords = {Adversarial information retrieval, Artificial Intelligence, Graph centralities, IF4.201, Information inference, Online social networks, Q1, machine learning, social network analysis, social networks}, pages = {104129}, } ']

[u' @article{sanchez-corcuera_smart_2019, title = {Smart cities survey: {Technologies}, application domains and challenges for the cities of the future}, volume = {15}, issn = {1550-1477}, shorttitle = {Smart cities survey}, url = {https://doi.org/10.1177/1550147719853984}, doi = {10.1177/1550147719853984}, abstract = {The introduction of the Information and Communication Technologies throughout the last decades has created a trend of providing daily objects with smartness, aiming to make human life more comfortable. The paradigm of Smart Cities arises as a response to the goal of creating the city of the future, where (1) the well-being and rights of their citizens are guaranteed, (2) industry and (3) urban planning is assessed from an environmental and sustainable viewpoint. Smart Cities still face some challenges in their implementation, but gradually more research projects of Smart Cities are funded and executed. Moreover, cities from all around the globe are implementing Smart City features to improve services or the quality of life of their citizens. Through this article, (1) we go through various definitions of Smart Cities in the literature, (2) we review the technologies and methodologies used nowadays, (3) we summarise the different domains of applications where these technologies and methodologies are applied (e.g. health and education), (4) we show the cities that have integrated the Smart City paradigm in their daily functioning and (5) we provide a review of the open research challenges. Finally, we discuss about the future opportunities for Smart Cities and the issues that must be tackled in order to move towards the cities of the future.}, language = {en}, number = {6}, urldate = {2019-06-10}, journal = {International Journal of Distributed Sensor Networks}, author = {S\xe1nchez-Corcuera, Ruben and Nu\xf1ez-Marcos, Adri\xe1n and Sesma-Solance, Jesus and Bilbao-Jayo, Aritz and Mulero, Rub\xe9n and Zulaika, Unai and Azkune, Gorka and Almeida, Aitor}, month = jun, year = {2019}, keywords = {Artificial Intelligence, IF1.151, IoT, Q4, Survey, architecture, co-creation, e-government, futuraal, smart cities}, pages = {1550147719853984}, } ']

[u' @inproceedings{zulaika_zurimendi_enhancing_2018, address = {Punta Cana, Republica Dominicana}, title = {Enhancing {Profile} and {Context} {Aware} {Relevant} {Food} {Search} through {Knowledge} {Graphs}}, volume = {2}, isbn = {2504-3900}, doi = {10.3390/proceedings2191228}, abstract = {Foodbar is a Cloud-based gastroevaluation solution, leveraging IBM Watson cognitive services. It brings together machine and human intelligence to enable cognitive gastroevaluation of \u201ctapas\u201d or \u201cpintxos\u201d , i.e., small miniature bites or dishes. Foodbar matchmakes users\u2019 profiles, preferences and context against an elaborated knowledge graph based model of user and machine generated information about food items. This paper reasons about the suitability of this novel way of modelling heterogeneous, with diverse degree of veracity, information to offer more stakeholder satisfying knowledge exploitation solutions, i.e., those offering more relevant and elaborated, directly usable, information to those that want to take decisions regarding food in miniature. An evaluation of the information modelling power of such approach is performed highlighting why such model can offer better more relevant and enriched answers to natural language questions posed by users.}, booktitle = {Proceedings}, publisher = {MDPI}, author = {Zulaika Zurimendi, Unai and Lopez-de-Ipina, Diego and Gutierrez, Asier}, month = dec, year = {2018}, keywords = {Artificial Intelligence, Knowledge Representation and Management, data models, foodbar2, knowledge graphs, machine learning, recommendation systems, smart cities, software architectures}, pages = {1228}, } ']